Geometric Modeling of EEG Signals in Alzheimer Patients

The project's goal is to classify test subjects into two groups: control and patients based on their EEG signals. The secondary goal is to determine the severity of the disease among the patients.
The methods in use in this project are Manifold Learning and specifically Diffusion Maps. These methods were adapted to the problem, given the EEG data, and implemented in the Time Domain, Frequency Domain and using Scattering Transform.
Alzheimer disease is characterized by damaged neuron connections that create unique Alpha waves patterns in the brain. Therefore, we examined different BPF in order to filter noise and classify mainly according to Alpha and\or Theta waves (Theta waves are responsible to learning and memory process).
The BPF improved the results (better classification).
After using the mentioned methods in the time, frequency and scattering domains we managed to get good separation between different people. However, we didn't get adequate classification between patients and control group. Therefore, we explored a new method of invariant matric which is meant to reduce the variance within a classification group.

Geometric Modeling of EEG Signals in Alzheimer Patients

 

Geometric Modeling of EEG Signals in Alzheimer Patients
Geometric Modeling of EEG Signals in Alzheimer Patients